Based on the use of the proximal gradient descent algorithm (as the \(L_1\)  norm is non-smooth) [1]}, (REF ) can be solved by the following iterative thresholding algorithm to emphasize the sparsity of the solutions:
\(X_i^{(t)} &= W_i^{(t)} - \frac{1}{Lip}\bigtriangledown J(W_i^{(t)}) \nonumber \\W_i^{(t+1)} &= S_{\frac{\alpha }{Lip}} (X_i^{(t)})\) 
